scholarly journals Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xianzhao Yang ◽  
Gengguo Cheng ◽  
Huikang Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Observation Interval ◽  
Extreme Points ◽  
Decomposition Algorithm ◽  
Complex Signal ◽  
Complex Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Average Value ◽  
Mode Decomposition

Hilbert-Huang transform is widely used in signal analysis. However, due to its inadequacy in estimating both the maximum and the minimum values of the signals at both ends of the border, traditional HHT is easy to produce boundary error in empirical mode decomposition (EMD) process. To overcome this deficiency, this paper proposes an enhanced empirical mode decomposition algorithm for processing complex signal. Our work mainly focuses on two aspects. On one hand, we develop a technique to obtain the extreme points of observation interval boundary by introducing the linear extrapolation into EMD. This technique is simple but effective in suppressing the error-prone effects of decomposition. On the other hand, a novel envelope fitting method is proposed for processing complex signal, which employs a technique of nonuniform rational B-splines curve. This method can accurately measure the average value of instantaneous signal, which helps to achieve the accurate signal decomposition. Simulation experiments show that our proposed methods outperform their rivals in processing complex signals for time frequency analysis.

scholarly journals An Improvement in Representation of Audio Signal in Time-Frequency Plane using EMD-2TEMD Based Approach

2016 ◽  
Vol 44 ◽  
pp. 141-150
Author(s):  
Kazi Mahmudul Hassan ◽  
Md. Ekramul Hamid ◽  
Takayoshi Nakai
Keyword(s):  
Empirical Mode Decomposition ◽  
Source Separation ◽  
Audio Signal ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Separation Result ◽  
Mode Decomposition ◽  
Recent Approach ◽  
Non Stationary Signal

This study proposed an enhanced time-frequency representation of audio signal using EMD-2TEMD based approach. To analyze non-stationary signal like audio, timefrequency representation is an important aspect. In case of representing or analyzing such kind of signal in time-frequency-energy distribution, hilbert spectrum is a recent approach and popular way which has several advantages over other methods like STFT, WT etc. Hilbert-Huang Transform (HHT) is a prominent method consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). An enhanced method called Turning Tangent empirical mode decomposition (2T-EMD) has recently developed to overcome some limitations of classical EMD like cubic spline problems, sifting stopping condition etc. 2T-EMD based hilbert spectrum of audio signal encountered some issues due to the generation of too many IMFs in the process where EMD produces less. To mitigate those problems, a mutual implementation of 2T-EMD & classical EMD is proposed in this paper which enhances the representation of hilbert spectrum along with significant improvements in source separation result using Independent Subspace Analysis (ISA) based clustering in case of audio signals. This refinement of hilbert spectrum not only contributes to the future work of source separation problem but also many other applications in audio signal processing.

Application of Hilbert–Huang Transform With Improved Ensemble Empirical Mode Decomposition in Nonlinear Flight Dynamic Mode Characteristics Estimation

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
S. Abolfazl. Mokhtari ◽  
Mehdi. Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Test Data ◽  
Flight Test ◽  
Ensemble Empirical Mode Decomposition ◽  
Dynamic Mode ◽  
Original Signal ◽  
Complex Signals ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Highly Nonlinear

Identification of aircraft flight dynamic modes has been implemented by adopting highly nonlinear flight test data. This paper presents a new algorithm for identification of the flight dynamic modes based on Hilbert–Huang transform (HHT) due to its superior potential capabilities in nonlinear and nonstationary signal analysis. Empirical mode decomposition and ensemble empirical mode decomposition (EEMD) are the two common methods that apply the HHT transform for decomposition of the complex signals into instantaneous mode frequencies; however, experimentally, the EMD faces the problem of “mode mixing,” and EEMD faces with the signal precise reconstruction, which leads to imprecise results in the estimation of flight dynamic modes. In order to overcome (handle) this deficiency, an improved EEMD (IEEMD) algorithm for processing of the complex signals that originate from flight data record was introduced. This algorithm disturbing the original signal using white Gaussian noise, IEEMD, is capable of making a precise reconstruction of the original signal. The second improvement is that IEEMD performs signal decomposition with fewer number of iterations and less complexity order rather than EEMD. This algorithm has been applied to aircraft spin maneuvers flight test data. The results show that implication of IEEMD algorithm on the test data obtained more precise signal extractions with fewer iterations in comparison to EEMD method. The signal is reconstructed by summing the flight modes with more accuracy respect to the EEMD. The IEEMD requires a smaller ensemble size, which results in saving of a significant computational cost.

scholarly journals Epileptic Seizures Detection based on Empirical Mode Decomposition and Hilbert-Huang transform of EEG Signal

2015 ◽  
Vol 1 (2) ◽  
pp. 295
Author(s):  
Mokhtar Mohammadi ◽  
Aso M. Darwesh
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizure ◽  
Spectral Energy ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. We present a new method for classification of ictal and seizure-free intracranial EEG recordings. The proposed method uses the application of multivariate empirical mode decomposition (MEMD) algorithm combines with the Hilbert transform as the Hilbert-Huang transform (HHT) and analyzing spectral energy of the intrinsic mode function of the signal. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signals time-frequency-energy distributions. Classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. The algorithm was tested in 6 intracranial channels EEG records acquired in 9 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.

A New Spectral Representation of Earthquake Recordings Using an Improved Hilbert-Huang Transform

2011 ◽  
Vol 255-260 ◽  
pp. 1671-1675
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou
Keyword(s):  
Empirical Mode Decomposition ◽  
Spectral Representation ◽  
Low Frequency ◽  
Accurate Information ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Marginal Spectrum

A new spectral representation method of earthquake recordings using an improved Hilbert-Huang transform (HHT) is proposed in the paper. Firstly, the problem that the intrinsic mode functions (IMFs) decomposed by the empirical mode decomposition (EMD) in HHT is not exactly orthogonal is pointed out and improved through the Gram-Schmidt orthogonalization method which is referred as the orthogonal empirical mode decomposition (OEMD). Combined the OEMD and the Hilbert transform (HT) which is referred as the improved Hilbert-Huang transform (IHHT), the orthogonal intrinsic mode functions (OIMFs) and the orthogonal Hilbert spectrum (OHS) and the orthogonal Hilbert marginal spectrum (OHMS) are obtained. Then, the IHHT has been applied for the analysis of the El Centro earthquake recording. The obtained spectral representation result shows that the OHS gives more detailed and accurate information in a time–frequency–energy presentation than the Hilbert spectrum (HS) and the OHMS gives more faithful low-frequency energy presentation than the Fourier spectrum (FS) and the Hilbert marginal spectrum (HMS).

Comparison on Turnovers of Agricultural Products Futures Based on Hilbert-Huang Transform

2014 ◽  
Vol 989-994 ◽  
pp. 2713-2718
Author(s):  
Qing Bin Han ◽  
Hai Li Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Agricultural Products ◽  
Analysis Approach ◽  
Commodity Exchange ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stationary Signals

In order to distinguish the different patterns and evolving trends on turnovers of agricultural products futures between Zhengzhou Commodity Exchange (Z-CE) and Dalian Commodity Exchange (D-CE), a novel time-frequency analysis approach, i.e. Hilbert-Huang transform (HHT), is investigated in this paper. Firstly, Hilbert-Huang transform is briefly introduced. Secondly, two different non-stationary signals of turnover of agricultural products futures from 2009 to 2013 coming from Z-CE and D-CE are described in Empirical Mode Decomposition (EMD). With these results, the two signals are distinctly different from each other. It is proved that the technique of HHT is effective for the purpose of distinction of turnover of agricultural products futures in commodity exchanges.

Application of Borehole Radar Data Processing Based on Empirical Mode Decomposition

2019 ◽  
Vol 24 (3) ◽  
pp. 409-418
Author(s):  
Xuzhou Zuo ◽  
Chunguang Ma ◽  
Jianping Xiao ◽  
Qing Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Ultra Wideband ◽  
Complex Signal ◽  
Complex Signals ◽  
Processing Scheme ◽  
Mode Decomposition ◽  
Borehole Radar ◽  
Radar Echo ◽  
Time Distance ◽  
Emd Method

Borehole Radar (BHR) uses ultra-wideband electromagnetic (EM) waves to image discontinuities in formations. It has been a major bottleneck to extend BHR applications to obtain a clear and high-resolution radar profile in a complex and noisy environment, which increases ambiguity in the geology interpretation. To avoid this increased ambiguity in the geology interpretation, we proposed a scheme based on the empirical mode decomposition (EMD) and complex signal analysis theory to process the BHR data with low signal to noise ratio (SNR). The scheme includes four steps. First, the original radar profile is pre-processed to avoid mode confusion and noise interference to the radar echo. Next, the EMD method is used to process a single-channel radar dataset and to analyze the frequency components of the radar signal. Various intrinsic modes of the pre-processing radar profile are also obtained by using EMD. Finally, we reconstruct the intrinsic mode profile, which contains information about the formation, calculate the complex signals of the reconstructed radar profile using the Hilbert transform, extract the three instantaneous attributes (instantaneous amplitude, instantaneous phase, and instantaneous frequency), and draw the separate instantaneous attributes profiles. This processing scheme provides both the conventional time-distance profile also in addition to the three instantaneous attributes. The additional attributes reduce ambiguity when evaluating the original radar profile and avoid the deviation relying solely on a conventional time-distance profile. An actual radar profile, which was obtained by a BHR system in a limestone fracture zone, is used to verify the effectiveness of instantaneous attributes for improving interpretation accuracy. The results demonstrate that the EMD method is superior in processing the BHR signal under a low SNR and has the capability to separate the high-low components of the radar echo effectively.

scholarly journals Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD

2018 ◽  
Vol 8 (10) ◽  
pp. 1801 ◽  
Author(s):  
Wenchao Li ◽  
Gangyao Kuang ◽  
Boli Xiong
Keyword(s):  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Radar Data ◽  
Signal Component ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Doppler Signals ◽  
Radar Signals ◽  
Non Stationary Signal

Micro-Doppler signals analysis has been emerging as an important topic in target identification, and relative research has been focusing on features extraction and separation of the radar signals. As a time-frequency representation, the Hilbert-Huang transform (HHT) could extract the accurate instantaneous micro-Doppler signature from the radar signals by empirical mode decomposition and Hilbert transform. However, HHT has the shortcoming that it cannot decompose the signals with close-frequency components. To solve this problem, an innovative decomposition method for multicomponent micro-Doppler signals based on Hilbert–Huang transform and analytical mode decomposition (HHT-AMD) is proposed. In this method, the multicomponent micro-Doppler signals are firstly decomposed by empirical mode decomposition, and the decomposed signal components are transformed by Hilbert transform to get the Hilbert-Huang spectrum and marginal spectrum. Through the spectrum processing, we get the frequency distribution of each signal component. The next step is to judge whether there exists frequency aliasing in each signal component. If there is aliasing, the AMD method is used to decompose the signal until all the decomposed signals are mono-component signals. Evaluation considerations are covered with numerical simulations and experiments on measured radar data. The results demonstrate that compared with conventional HHT, the proposed method yields accurate decomposition for multicomponent micro-Doppler signals and improves the robustness of decomposition. The method presented here can also be applied in various settings of non-stationary signal analysis and filtering.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

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